Attenuation estimation using ultrasound

ABSTRACT

Systems and methods for attenuation measuring using ultrasound. In various embodiments, echo data corresponding to a detection of echoes of one or more ultrasound signals transmitted into tissue are received. The echoes can be received from a range of depths of the tissue. Spectral measurements across the range of depths of the tissue are obtained using the echo data. Attenuation characteristics of the tissue across the range of depths of the tissue can be estimated using the spectral measurements across the range of depths of the tissue. Specifically, the attenuation characteristics of the tissue can be estimated using the spectral measurements and known spectral characteristics of the one or more ultrasound signals transmitted into the tissue.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/571,859, filed Jan. 10, 2022, for ATTENUATION ESTIMATION USINGULTRASOUND, which is a continuation of U.S. application Ser. No.16/242,287, filed Jan. 8, 2019, for ATTENUATION ESTIMATION USINGULTRASOUND, now U.S. Pat. No. 11,275,159, each of which is incorporatedherein by reference.

TECHNICAL FIELD

This disclosure relates to ultrasound systems and methods and, inparticular, to attenuation estimation across a range of depths of atissue using ultrasound.

BACKGROUND OF THE INVENTION

Ultrasound imaging is widely used for examining a wide range ofmaterials and objects across a wide array of different applications.Ultrasound imaging provides a fast and easy tool for analyzing materialsand objects in a non-invasive manner. As a result, ultrasound imaging isespecially common in the practice of medicine as an ailment diagnosis,treatment, and prevention tool. Specifically, because of its relativelynon-invasive nature, low cost and fast response time, ultrasound imagingis widely used throughout the medical industry to diagnose and preventailments. Further, as ultrasound imaging is based on non-ionizingradiation, it does not carry the same risks as other diagnosis imagingtools, such as X-ray imaging or other types of imaging systems that useionizing radiation.

In general, ultrasound imaging is accomplished by generating anddirecting ultrasonic sound waves into a material of interest, first in atransmit phase and subsequently in a receive phase. During the transmitphase, an ultrasonic signal is transmitted into a material of interestby applying continuous or pulsed electronic signals. During the receivephase, reflections generated by boundaries between dissimilar materialsare received by receiving devices, such as transducers, and converted toelectrical signals. Signals can then be processed to determine thelocations of the echo sources. The resulting data can be used to displayimages of inside a material of interest, e.g. by displaying images usinga display device, such as a monitor.

Ultrasound imaging can offer a wealth of clinical information.Specifically, ultrasound imaging can be used in abdominal ultrasound (tovisualize abdominal tissues and organs), bone sonometry (to assess bonefragility), breast ultrasound (to visualize breast tissue), Dopplerfetal heart rate monitors (to listen to a fetal heart beat), Dopplerultrasound (to visualize blood flow through a blood vessel, organs, orother structures), echocardiogram (to view a heart), fetal ultrasound(to view a fetus in pregnancy), ultrasound-guided biopsies (to collect asample of tissue), ophthalmic ultrasound (to visualize ocularstructures) and ultrasound-guided needle placement (in blood vessels orother tissues of interest). Ultrasound imaging has also been used indescribing various disease states, such as diseases of the liver,breast, prostate, thyroid or other organs through single measurements ofstiffness or shear wave velocity.

The attenuation of sound within tissue has been shown to vary based ontissue type as well as within a particular tissue type based on diseasestate. The ability to measure as well as track over time the attenuationproperties of tissue within various organs has the potential to enablephysicians to monitor the progression of a number of diseases. Inaddition, it enables the physician to measure the efficacy of thecurrent therapy with the possibility to alleviate the need to acquireregular tissue biopsies. Using ultrasound to measure attenuationproperties of tissue is advantageous due to the non-invasive nature ofultrasound. There therefore exist needs for systems and methods formeasuring attenuation properties of tissue using ultrasound.

In order to achieve these measurements using ultrasound, a number ofchallenges exist. For example, limited bandwidth of transducers,variability across transducers, variability within a transducer, signalto noise ratio issues, and the like can lead to problems in measuringattenuation characteristics of tissue using ultrasound. Specifically,even though the transducer is a significant limitation of ultrasoundsystem performance, the interactions of the sound waves with the tissuealso results in reduced performance at depth due to a reduction ofbandwidth from frequency dependent attenuation properties of tissue.However, attenuation characteristics of tissue across a range of depthsof the tissue are the properties that are utilized by physicians tomonitor progression of a number of diseases. There therefore exist needsfor systems and methods that facilitate gathering attenuationcharacteristics of tissue across a range of depths of the tissue usingultrasound.

SUMMARY

According to various embodiments, a method for estimating attenuationcharacteristics of a tissue using ultrasound includes receiving echodata corresponding to a detection of echoes of one or more ultrasoundsignals transmitted into the tissue. The echoes can be received from arange of depths of the tissue. Spectral measurements across the range ofdepths of the tissue are obtained using the echo data. Attenuationcharacteristics of the tissue across the range of depths of the tissuecan be estimated using the spectral measurements across the range ofdepths of the tissue. Specifically, the attenuation characteristics ofthe tissue can be estimated using the spectral measurements and knownspectral characteristics of the one or more ultrasound signalstransmitted into the tissue.

In certain embodiments, a system for estimating attenuationcharacteristics of a tissue using ultrasound includes an ultrasoundtransducer and a main processing console. The ultrasound transducer canbe configured to transmit one or more ultrasound signals into thetissue. Additionally, the ultrasound transducer can be configured todetect echoes of the one or more ultrasound signals from the tissueacross a range of depths of the tissue. The main processing console canbe configured to receive echo data corresponding to a detection of theechoes of the one or more ultrasound signals transmitted into thetissue. Additionally, the main processing console can be configured toobtain spectral measurements across the range of depths of the tissueusing the echo data. Subsequently, the main processing console can beconfigured to estimate attenuation characteristics of the tissue acrossthe range of depths of the tissue using the spectral measurements acrossthe range of depths of the tissue. Specifically, the main processingconsole can be configured to estimate the attenuation characteristicsusing the spectral measurements and known spectral characteristics ofthe one or more ultrasound signals transmitted into the tissue.

In various embodiments, a system for estimating attenuationcharacteristics of a tissue using ultrasound includes one or moreprocessors and a computer-readable medium providing instructionsaccessible to the one or more processors to cause the one or moreprocessors to perform operations including receiving echo datacorresponding to a detection of echoes of one or more ultrasound signalstransmitted into the tissue. The ultrasound signals can be received froma range of depths of the tissue. The instructions can further cause theone or more processors to obtain spectral measurements across the rangeof depths of the tissue using the echo data. Additionally, theinstructions can cause the one or more processors to estimateattenuation characteristics of the tissue across the range of depths ofthe tissue using the spectral measurements across the range of depths ofthe tissue. Specifically, the instructions can cause the one or moreprocessors to estimate the attenuation characteristics of the tissueusing the spectral measurements and known spectral characteristics ofthe one or more ultrasound signals transmitted into the tissue.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of an ultrasound system.

FIG. 2 is a flowchart illustrating an example method for estimatingattenuation characteristics of a tissue using ultrasound.

FIG. 3 is a diagram illustrating a process for generating a set ofattenuation coefficients.

FIG. 4 shows a flowchart of a method for obtaining an estimate of depthvarying spectral characteristics of a transmitted waveform.

FIG. 5 is a graph illustrating a plot of the mean of the varying depthspectral estimates of a transducer, in accordance to various aspects ofthe subject technology.

FIG. 6 is a set of graphs including a set of plots showing the spectralcharacteristics of a given depth from data uncompensated for transducerbandwidth limitation and one compensated.

FIG. 7 shows example attenuation data estimates.

DETAILED DESCRIPTION

Ultrasound transducers typically have a limited bandwidth and spectralresponse variability. Certain aspects of the transducer technology maybe improved by using single crystal transducers, composite materialtransducers, capacitive micromachined ultrasonic transducers (CMUTs),piezoelectric micromachined ultrasonic transducers (PMUTs), or otherimprovements. As a result, there is a large variety of transducer types,each with different characteristics, performance, and shortcomings.However, there is still a great need for further improving ultrasoundperformance. Aspects of the subject technology provide for techniquesand systems for measuring and shaping a spectral response, which resultsin an increased spectral bandwidth, e.g. for estimating attenuationcharacteristics of tissue. Furthermore, the subject technology providesa robust solution that can be applied to a wide variety of differenttransducer types and is compatible with other improvements in transducertechnology.

The properties of a medium that the ultrasonic pulse travels through(e.g., the material of interest, such as organic tissue) may also affectthe ultrasonic echo and distorts the resulting ultrasound image. Forexample, the medium may have an attenuation property and, in many cases,the attenuation property may be dependent on frequency. In other words,the medium may attenuate lower frequency ultrasonic signals (or thelower frequency portions of an ultrasonic signal) less than higherfrequency ultrasonic signals (or the higher frequency portions of theultrasonic signal). Furthermore, the rates at which ultrasonic signalsare attenuated may vary based on the properties of the medium such ascomposition, density, layers, location of objects in the medium, etc.This may be especially true when the medium is organic tissue that mayinclude a number of layers and/or components that may each havedifferent characteristics. Furthermore, these layers and/or componentsmay be dispersed throughout the tissue in a non-uniform manner. Aspectsof the subject technology also provide for techniques and systems thatcompensate for the frequency dependent attenuation property of themedium and how the property affects attenuation at different depths.

Some aspects of the subject technology relate to processes, systems,and/or instructions stored on machine-readable medium that providetechnical solutions to the above technical problems and others. Forexample, according to some embodiments, a transducer may be configuredto transmit a pulse signal into a tissue. The pulse signal may be abroad spectrum long time coded calibration pulse, for example a chirp,(as opposed to an imaging pulse of short time but broad spectrum).Ultrasound echoes are generated based on reflections of the transmittedpulse signal on boundaries, objects, or other components in the tissueat various depths. These echoes may be received by the transducer andtranslated into echo data.

A set of fast Fourier transforms may be generated based on the receivedecho data. Each fast Fourier transform in the set may be associated witha particular depth value or range of depths. These depth values andranges may be disjoint or, in some cases, overlap. A point estimates fora frequency dependent filtering coefficient of a spectral response forthe each fast Fourier transform may also be generated. These pointestimates may be used to form a line. In some cases, normalization stepsmay also be taken to smooth out the line. For example, a least meanssquare fit process may be used.

A set of attenuation coefficients may be extracted from the line formedby the point estimates and these attenuation coefficients may be used tocompensate for spectral response limitations of the transducer or forfrequency dependent attenuation of the tissue of interest. For example,a second pulse signal may be transmitted into the tissue. This secondpulse, in some cases, may be an imaging pulse that is shorter in timebut still has a broad bandwidth than the initial calibration pulse usedabove. Echo data for the second pulse signal may be received and used,along with the set of attenuation coefficients, to generate an image ofthe tissue. For example, the attenuation coefficients may be used asinverse filters for the image data.

Although some embodiments may be discussed with respect to organictissues, other mediums, materials, or objects of interest may similarlybe used. Furthermore, although fast Fourier transforms are discussedwith respect to some embodiments, other algorithms or signal conversionmethods may also be used.

Various aspects of certain embodiments may be implemented usinghardware, software, firmware, or a combination thereof. As used herein,a software module or component may include any type of computerinstruction or computer executable code located within or on acomputer-readable storage medium. A software module may, for instance,comprise one or more physical or logical blocks of computerinstructions, which may be organized as a routine, program, object,component, data structure, etc., that performs one or more tasks orimplements particular abstract data types.

In certain embodiments, a particular software module may comprisedisparate instructions stored in different locations of acomputer-readable storage medium, which together implement the describedfunctionality of the module. Indeed, a module may comprise a singleinstruction or many instructions, and may be distributed over severaldifferent code segments, among different programs, and across severalcomputer-readable storage media. Some embodiments may be practiced in adistributed computing environment where tasks are performed by a remoteprocessing device linked through a communications network.

The embodiments of the disclosure will be best understood by referenceto the drawings, wherein like parts are designated by like numeralsthroughout. The components of the disclosed embodiments, as generallydescribed and illustrated in the figures herein, could be arranged anddesigned in a wide variety of different configurations. Furthermore, thefeatures, structures, and operations associated with one embodiment maybe applicable to or combined with the features, structures, oroperations described in conjunction with another embodiment. In otherinstances, well-known structures, materials, or operations are not shownor described in detail to avoid obscuring aspects of this disclosure.

Thus, the following detailed description of the embodiments of thesystems and methods of the disclosure is not intended to limit the scopeof the disclosure, as claimed, but is merely representative of possibleembodiments. In addition, the steps of a method do not necessarily needto be executed in any specific order, or even sequentially, nor need thesteps be executed only once.

FIG. 1 illustrates an example of an ultrasound system 100. Theultrasound system 100 shown in FIG. 1 is merely an example system and invarious embodiments, the ultrasound system 100 can have less componentsor additional components. Specifically, the ultrasound system 100 can bean ultrasound system where the receive array focusing unit is referredto as a beam former 102, and image formation can be performed on ascanline-by-scanline basis. System control can be centered in the mastercontroller 104, which accepts operator inputs through an operatorinterface and in turn controls the various subsystems. For each scanline, the transmitter 106 generates a radio-frequency (RF) excitationvoltage pulse waveform and applies it with appropriate timing across thetransmit aperture (defined by a sub-array of active elements) togenerate a focused acoustic beam along the scan line. RF echoes receivedby the receive aperture 108 of the transducer 110 are amplified andfiltered by the receiver 108, and then fed into the beam former 102,whose function is to perform dynamic receive focusing; i.e., to re-alignthe RF signals that originate from the same locations along various scanlines.

The image processor 112 can perform processing specific to activeimaging mode(s) including 2D scan conversion that transforms the imagedata from an acoustic line grid to an X-Y pixel image for display. ForSpectral Doppler mode, the image processor 112 can perform wallfiltering followed by spectral analysis of Doppler-shifted signalsamples using typically a sliding FFT-window. The image processor 112can also generate the stereo audio signal output corresponding toforward and reverse flow signals. In cooperation with the mastercontroller 104, the image processor 112 also can format images from twoor more active imaging modes, including display annotation, graphicsoverlays and replay of cine loops and recorded timeline data.

The cine buffer 114 provides resident digital image storage for singleimage or multiple image loop review, and acts as a buffer for transferof images to digital archival devices. On most systems, the video imagesat the end of the data processing path can be stored to the cine memory.In state-of-the-art systems, amplitude-detected, beam formed data mayalso be stored in cine memory 114. For spectral Doppler, wall-filtered,baseband Doppler 1/Q data for a user-selected range gate can be storedin cine memory 114. Subsequently, the display 11 can display ultrasoundimages created by the image processor 112 and/or images using datastored in the cine memory 114.

The beam former 102, the master controller 104, the image processor, thecine memory 114, and the display can be included as part of a mainprocessing console 118 of the ultrasound system 100. In variousembodiments, the main processing console 118 can include more or fewercomponents or subsystems. The ultrasound transducer 110 can beincorporated in an apparatus that is separate from the main processingconsole 118, in a separate apparatus that is wired or wirelesslyconnected to the main processing console 118. This allows for easiermanipulation of the ultrasound transducer 110 when performing specificultrasound procedures on a patient.

As noted above, ultrasound systems operate on a limited bandwidth and/orhave a limited reliable spectral response. The properties of the medium(e.g., the tissue) that the ultrasound system is directed at alsodistort ultrasound signals and, as a result, distort any resultingultrasound image. Various embodiments of the subject technology aredirected to improving these and other technical issues with ultrasoundtechnology.

According to various embodiments, spectral measurements of the signalsat various depths can be made and an estimate of the frequency dependentattenuation coefficient along with the passband response of thetransducer can be obtained. Given that the attenuation coefficient oftissue will attenuate higher frequencies at a different rate (e.g., afaster rate) than lower frequencies the overall attenuation of thespectral response from the initial transmit spectral pulse is filteredinto a lower and lower center frequency signal with narrower andnarrower bandwidth as it propagates through the tissue. As the beams areformed the system can interrogate areas that represent primarily speckleso that a clean spectral estimate can be obtained at each depth.

According to some embodiments, the spectral estimates may be overlappingin depth to various extents. Several beam formed lines may also be usedto get multiple spectral estimates and these estimates may be normalizedor averaged to improve the overall signal to noise ratio (SNR) at eachdepth. Alternatively or additionally, the data may be averaged in placein a coherent manner to improve the overall SNR as well.

Once these spectral estimates are calculated at various depths, theknown spectral characteristics of the transmit pulse along with themeasured spectral estimates at depth can be used to estimate thetransducer transfer function as well as the attenuation properties ofthe tissues with depth. This information can then be used to generatedepth dependent inverse filters to compensate to some extent to thenarrowing bandwidth and rate of frequency downshift of the waveform asit propagates through the tissue resulting in improving the systemdetail resolution performance characteristics at depth. The system mayfurther be configured to measure the spectral SNR at the various depthsand automatically determine and adjust how much gain should be appliedto the signals at various depths and/or frequencies to ensure that it isprimarily signal being compensated instead of just the noise.

According to some embodiments, the image processing of the incoming datafrom the reflected transmit pulse may include forming an image based onthe one or more processing algorithms. These image forming algorithmsmay include beam formation, synthetic aperture techniques, or adaptiveimage formation techniques. This data could be obtained with either aunique transmit pulse that is used to obtain a more comprehensivespectral bandwidth of the system and tissue or with a standard imagingpulse. In some embodiments, a first estimate based on a unique pulse forlarge adjustment may be generated and a standard transmit pulse may beused to generate subsequent estimates to track changes for fineadjustments. Once the image has been formed, a gain compensation may beapplied based on previous measurements. If there have been no previousmeasurements than an initial estimate of the gain compensation may becalculated without compensation.

A moving block fast Fourier transform (FFT) in the range direction maybe performed in accordance with various embodiments. These transformsmay be generated with overlapping ranges so that a smooth estimate ofthe parameters and compensation required can be made. In otherembodiments, the FFTs may not have any overlap in range. Several FFTsalong different range lines can be used to improve the overall estimateas their spectral profile can be averaged or the system can alsocoherently average several transmit/receive cycles in place to improvethe overall SNR of the signal. The transducer and system parametercompensation may be applied to the signals in the spectral domain sothat a slope calculation of the attenuation coefficient can becalculated. In some embodiments, once there is an estimate of theattenuation coefficient is estimated on a point basis, a moving averageand/or least means squares fit can be applied to get a smoother varyingestimate of the tissue attenuation.

According to various embodiments, a method for estimating attenuationcharacteristics of a tissue using ultrasound includes receiving echodata corresponding to a detection of echoes of one or more ultrasoundsignals transmitted into the tissue. The echoes can be received from arange of depths of the tissue. Spectral measurements across the range ofdepths of the tissue are obtained using the echo data. Attenuationcharacteristics of the tissue across the range of depths of the tissuecan be estimated using the spectral measurements across the range ofdepths of the tissue. Specifically, the attenuation characteristics ofthe tissue can be estimated using the spectral measurements and knownspectral characteristics of the one or more ultrasound signalstransmitted into the tissue.

In certain embodiments, a system for estimating attenuationcharacteristics of a tissue using ultrasound includes an ultrasoundtransducer and a main processing console. The ultrasound transducer canbe configured to transmit one or more ultrasound signals into thetissue. Additionally, the ultrasound transducer can be configured todetect echoes of the one or more ultrasound signals from the tissueacross a range of depths of the tissue. The main processing console canbe configured to receive echo data corresponding to a detection of theechoes of the one or more ultrasound signals transmitted into thetissue. Additionally, the main processing console can be configured toobtain spectral measurements across the range of depths of the tissueusing the echo data. Subsequently, the main processing console can beconfigured to estimate attenuation characteristics of the tissue acrossthe range of depths of the tissue using the spectral measurements acrossthe range of depths of the tissue. Specifically, the main processingconsole can be configured to estimate the attenuation characteristicsusing the spectral measurements and known spectral characteristics ofthe one or more ultrasound signals transmitted into the tissue.

In various embodiments, a system for estimating attenuationcharacteristics of a tissue using ultrasound includes one or moreprocessors and a computer-readable medium providing instructionsaccessible to the one or more processors to cause the one or moreprocessors to perform operations including receiving echo datacorresponding to a detection of echoes of one or more ultrasound signalstransmitted into the tissue. The ultrasound signals can be received froma range of depths of the tissue. The instructions can further cause theone or more processors to obtain spectral measurements across the rangeof depths of the tissue using the echo data. Additionally, theinstructions can cause the one or more processors to estimateattenuation characteristics of the tissue across the range of depths ofthe tissue using the spectral measurements across the range of depths ofthe tissue. Specifically, the instructions can cause the one or moreprocessors to estimate the attenuation characteristics of the tissueusing the spectral measurements and known spectral characteristics ofthe one or more ultrasound signals transmitted into the tissue.

FIG. 2 is a flowchart illustrating an example method 200 for estimatingattenuation characteristics of a tissue using ultrasound. The operationsof method 200 presented below are intended to be illustrative. In someembodiments, method 200 may be accomplished with one or more additionaloperations not described, and/or without one or more of the operationsdiscussed. Additionally, the order in which the operations of method 200are illustrated in FIG. 2 and described below is not intended to belimiting.

In some embodiments, method 200 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 200 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 200. For example,the method shown in FIG. 2 can be performed by an applicable ultrasoundsystem, a transducer system, computing device, or similar system.

At operation 202, echo data corresponding to a detection of echoes ofone or more ultrasound signals transmitted into the tissue is received.The echoes can be received from a range of depths of the tissue. As willbe discussed in greater detail later, various operations can beperformed to compensate for limitations of echo data received fromtissue at ever increasing depths within the tissue. In turn, this canallow for estimation of attenuation characteristics of the tissue acrossa range of depths of the tissue, thereby leading to improved treatmentand diagnosis of a patient. Specifically, doctors can more accuratelydiagnose and treat diseases when attenuation characteristics of thetissue are obtained across a range of depths of the tissue. The echodata can be generated by an applicable ultrasound system or component ofan ultrasound system, such as the ultrasound system 100 shown in FIG. 1and the transducer 110 and the main processing console 118 of theultrasound system 100 shown in FIG. 1 .

The echo data can be obtained with either a unique transmit pulse thatis used to obtain a more comprehensive spectral bandwidth of the systemand tissue when compared with a pulse used to perform standardultrasound imaging. Specifically, the one or more ultrasound signalstransmitted into the tissue can be formed by one or more broadbandpulses. More specifically, the ultrasound signals transmitted into thetissue can be formed by broadband pulses having a pulse length greaterthan the pulse lengths of ultrasound signals used in performingultrasound imaging. For example, a broad band spectral chirp pulse canbe used to generate the ultrasound signals transmitted into the tissue.This can ensure that an increased/maximum signal to noise ratio isobtained with a broad spectral bandwidth, e.g. when compared toultrasound pulses used in standard ultrasound imaging. In variousembodiments, an image formed from the echo data is not actuallydisplayed to a user. Accordingly, a long pulse and subsequent poor imagecharacteristics as a result of the long pulse are not an issue.Additionally, the visible imaging problems created by the long pulse arenot an issue as the attenuation from the skin line to the desired tissuetypically is several millimeters away. As a result, the visible imagingproblems created by the long pulse are satisfactory. While theultrasound signals are described as being created from one or morebroadband pulses, in various embodiments, the ultrasound signals used inmeasuring the attenuation characteristics of the tissue can be createdusing standard ultrasound imaging pulses.

At operation 204, spectral measurements across the range of depths ofthe tissue are obtained using the echo data. Spectral measurements canbe obtained using applicable signal processing techniques, such as thesignal/image processing techniques described herein. For example, and aswill be discussed in greater detail later, the spectral measurements canbe obtained based on the echo data using FFTs. Additionally, and as willbe discussed in greater detail later, these spectral measurements can beused to estimate attenuation characteristics of the tissue across therange of depths of the tissue. For example, spectral measurements of thesignals at various depths can be made and an estimate of the frequencydependent attenuation coefficient along with the passband response ofthe transducer can be obtained.

The echo data/spectral measurements can be filtered to identify spectralmeasurements across the range of depths of the tissue. Specifically, theecho data/spectral measurements can be filtered based on a depth of theone or more ultrasound signals within the tissue where the correspondingecho data is created. Subsequently, the spectral measurements can beobtained across, at least part of, the range of depths of the tissueusing the filtered echo data. For example, echo data created by theultrasound signals as the ultrasound signals travel deeper into thetissue can be filtered. Specifically, given that the attenuationcoefficient of tissue will attenuate higher frequencies at a quickerrate than lower frequencies, the overall attenuation of the spectralresponse from the initial transmit, spectral measurements/correspondingecho data can be filtered into a lower and lower center frequency signalwith narrower and narrower bandwidth as it propagates through thetissue, e.g. as the frequency decreases. In turn, the echo data can befiltered to decrease a lower central frequency of the echo data as theone or more ultrasound signals used to generate the echo data passfurther and further into the tissue along the range of depths of thetissue, e.g. away from the skin line.

Further, spectral measurements can be obtained based on identificationof areas in the range of depths of the tissue that are represented byspeckle. Specifically, additional spectral measurements can be obtained,e.g. continuously obtained, for areas that represent primarilyspeckle/above a certain threshold amount of speckle. As a result, aclean spectral estimate can be obtained at a plurality of depths withinthe range of depths, even in areas where speckle exists. Subsequently,the spectral estimates of the areas represented primarily by speckle canalso be used to estimate attenuation characteristics of the tissueacross a range of depths of the tissue.

Additionally, the spectral measurements/estimates can overlap to someextent within the range of depths of the tissue. Specifically, severalbeamformed lines can be used to get multiple spectralestimates/measurements that overlap. In turn, the spectral estimates,e.g. overlapping estimates, can be averaged to improve the overallsignal to noise ratio at each depth, e.g. when compared to spectralestimates that are not averaged. Specifically, averaging all or aportion of the spectral measurements can improve the overall signal tonoise ratio across the entire range of depths of the tissue.

Further, the spectral estimates can be averaged in place as well as in acoherent manner, e.g. as discussed previously to improve the overallsignal to noise ratio. Specifically, all or portions of the spectralestimates can be averaged, e.g. in place, regardless of whether thespectral estimates overlap or not, in order to improve the overallsignal to noise ratio across the range of depths of the tissue. Morespecifically, spectral estimates can be averaged based on theircorresponding depths within the tissue in order to improve the overallsignal to noise ratio. For example, spectral estimates at greaterdepths, e.g. deeper from the skin in the tissue, can be averaged toimprove the overall signal to noise ratio.

Additional processing can be applied to improve the overall signal tonoise ratio, e.g. processes used for synthetic transmit focusing toimprove the overall signal to noise ratio. Specifically, time varyinginverse filters can only compensate/estimate the attenuation to a finiteextent over time, because as the signals become increasingly attenuated,the desired signal at higher frequencies might be below the noise level.In order to counteract this problem, as discussed previously, thespectral measurements and/or echo data can be averaged in place toimprove the SNR at depth. In turn, this can compensate for the problemsof estimating attenuation at deeper levels and at higher bandwidths.Alternatively, the spectral signal to noise ratio can be measured overtime. Subsequently, gains to apply at the various depths to compensatefor the deficiencies in attenuation estimation can be identified, basedon the measured signal to noise ratios. In turn the corresponding gainscan be applied at the various depths/frequencies to ensure that aprimarily signal is compensated/used for estimating attenuation insteadof just noise.

As discussed previously, the spectral measurements can be identifiedusing FFTs. Specifically, one or more image can be created using echodata. Subsequently, a set of FFTs can be generated based on the one ormore images created using the echo data. Each FFT can be associated witha depth value in the range of depths of the tissue. In turn, a set ofpoint estimates for a frequency dependent filtering coefficient,included as part of the spectral measurements can be generated acrossthe range of depths of the tissue. Each frequency dependent filteringcoefficient can correspond to one or more depths within the range ofdepths of the tissue. Subsequently, and as will be discussed in greaterdetail later, the set of point estimates can be used to estimate a setof attenuation coefficients, representing attenuation levels of thetissue/attenuation characteristics of the tissue, across the range ofdepths of the tissue.

Applicable ultrasound image processing techniques can be utilized togenerate the one or more images from the echo data, e.g. for purposes ofidentifying the set of point estimates. Specifically, basic imageprocessing can be applied to the echo data generated from the ultrasoundsignals to form an image. For example, standard beamformation, syntheticaperture techniques, or adaptive image formation techniques can beapplied to the echo data to generate one or more images for purposes ofgenerating the set of point estimates across the range of depths of thetissue.

In identifying the set of point estimates using FFTs, once the one ormore images have been formed, a moving block FFT can be applied to theone or more images. Specifically, the moving block FFT can be applied inthe range direction of the one or more images to identify the set ofpoint of estimates. A range direction of the one or more images cancorrespond to a range of the depths within the tissue represented in theimages. For example, the moving block FFT can first be applied toportions of the one or more images that correspond to regions in thetissue closest to the surface of the tissue. Then, the moving block FFTcan be applied to portions of the one or more images representing everdecreasing depths of the tissue in the one or more images in order togenerate the set of point estimates.

A range direction of the transforms is typically taken with overlap ofthe previous FFT so that a smooth estimate of the parameters and otherapplicable compensation(s) can be made. However, one or more movingblock FFTs can be applied to different portions of the one or moreimages that do not overlap in range. For example, several moving blockFFTs can be applied along different range lines to improve the overallestimates in the set of point estimates. Specifically, spectral profilescan be averaged and/or several transmit/receive cycles can be averagedin place to improve the overall SNR of the signal. In turn, this canimprove the overall point estimates identified using FFTs.

At operation 206, attenuation characteristics of the tissue across therange of depths of the tissue can be estimated. Specifically,attenuation characteristics of the tissue can be measured using thespectral measurements and known spectral characteristics of the one ormore ultrasound signal transmitted into the tissue. Spectralcharacteristics, e.g. known spectral characteristics, can includeapplicable characteristics describing ultrasound signals transmittedinto a tissue. For example, spectral characteristics can includemagnitudes of an ultrasound signal across a range of frequencies.Attenuation characteristics of the tissue across the range of depths ofthe tissue can include attenuation levels/different attenuation levelsat each depth across the range of depths of tissue. Specifically, oncethe spectral measurements/estimates are identified for various depths,at operation 204, then the known spectral characteristics of thetransmit pulse/ultrasound signal(s) along with the measured spectralestimates at depth can be used to estimate the transducer transferfunction as well as the attenuation properties of the tissues withdepth. This information can then be used to estimate the attenuation ofthe tissue at various range depths as well as azimuthal locations.Subsequently, the estimated attenuation characteristics of the tissuecan be presented to a user, e.g. for purposes of diagnosing or managingtreatment of a disease.

In using FFTs to ultimately determine the attenuation characteristics ofthe tissue, a set of attenuation coefficients can be extracted based onthe set of point estimates for the frequency dependent filteringcoefficient. These attenuation coefficients can represent attenuationlevel/different attenuation levels across the range of depths of thetissue. The set of attenuation coefficients can be extracted based onthe set of point estimates, as will be discussed in greater detaillater, according to a slope of a line formed from the set of pointestimates. Further, the set of attenuation coefficients can be extractedbased on set of point estimates and the known spectral characteristicsof the one or more ultrasound signals transmitted into the tissue. Forexample, the set of point estimates can be compared to the knownspectral characteristics of the one or more ultrasound signalstransmitted into the tissue to determine differences between the pointestimates and the known spectral characteristics. These differences cancorrespond to different attenuation levels within the tissue across therange of depths of the tissue. Subsequently, the estimated attenuationcoefficients can be presented to a user, e.g. for purposes of diagnosingor managing treatment of a disease.

The varying estimate of the attenuation levels across the range ofdepths of the tissue can be identified based on the estimated set ofattenuation coefficients. Specifically, a moving average least meanssquares fit can be applied to the set of attenuation coefficients. togenerate a smooth slowly varying estimate of the tissue attenuationacross the range of depths of the tissue. Subsequently, the varyingestimate of the attenuation levels across the range of depths of thetissue can be presented to a user, e.g. for purposes of diagnosing ormanaging treatment of a disease.

FIG. 3 is a diagram illustrating a process for generating a set ofattenuation coefficients. In particular, FIG. 3 includes a graph 310 ofa pulse signal. In particular, the graph 310 illustrates the amplitude(in decibels dB) and frequency of a pulse signal, such as a broadspectrum or broadband calibration pulse. According to variousembodiments, a known broadband calibration pulse represented by graph310 may be transmitted by the ultrasound system for a period of timeinto a tissue. Echoes caused by reflections and interactions between thebroadband calibration pulse and the tissue at various depths may bereceived by the ultrasound system.

The calibration pulse may be filtered and distorted from interactionswith both the transducer of the ultrasound system as well as the tissue.For example, in the near field, the distortion and filter effect may beprimarily due to the transducer while, as the signal propagates throughvarious depths, the spectral filtering of the higher frequenciesincreases. This is illustrated in graphs 320, 321, 322, and 323. Morespecifically, the graphs 320, 321, 322, and 323 help illustrate thatdifferent tissue depths (e.g., the time needed for ultrasound pulses toreach the tissue depth and echo), affect the spectral filtering ofultrasound pulses. The higher frequencies of the calibration pulse arefiltered at a greater rate than the lower frequencies of the calibrationpulse. Furthermore, as depth increases in graphs 320, 321, 322, and 323,this effect increases in prominence.

Based on the received signals from various scan lines at various depths(as illustrated in graphs 320, 321, 322, and 323), a set of overlappingin range fast Fourier transforms (FFTs) may be calculated. The set ofFFTs is illustrated by graph 330. As illustrated by the graph 330, insome embodiments, the range of depths may overlap. In other embodiments,however, the depth values may not be ranges and/or may not overlap.

From this set of FFTs, point estimates of the slope of the line for thefrequency dependent filtering coefficient of the spectral response canbe calculated. These point estimates are illustrated by graph 340. Fromthese point estimates a least means squares fit can be performed acrossa sub set of points to obtain an improved estimate of regional values ofthe slope of the line for the frequency dependent filtering of thespectral response. The regional slope of the frequency dependentfiltering coefficient of the spectral response represents thecorresponding attenuation coefficient. As a result, a set of attenuationcoefficients may be extracted based on the set of point estimates. Thisis illustrated by graph 350, which shows 2 attenuation coefficientscalculated for the portion of the line shown in graph 340. Conceptually,these attenuation coefficients may represent a change in slope in theline for the frequency dependent filtering coefficient in graph 340.These attenuation coefficients may be used to calculate a rangedependent compensation filter to compensate for some of the spectralresponse reduction in the signal resulting in improved image performancefrom a detail resolution figure of merit measurement.

Various embodiments of the subject technology relate to obtaining anestimate of the depth varying spectral characteristics of thetransmitted waveform. For example, a known transmit pulse spectrum maybe transmitted and received at a range of depths of the tissue. Thedigitized signals may be processed in an image formation stage. A numberof image formation techniques are possible to be used ranging fromstandard digital beam formation, synthetic aperture techniques, andadaptive image formation techniques. If there are already estimates forthe gain compensation, those estimates may be applied. However, on afirst iteration of the process (or if no estimate is available), theover depth gain compensation will most likely default to a standardvalue, for example an identity function.

After the gain compensation is done, the gain compensated signals can beused to form an image to be displayed to the systems user. Additionally,or alternatively, these same signals may also use to calculate acorrection factor for the next iteration of the process. For example,the signals used for calculating the gain compensation and thecorrection factors may be used to calculate a set of spectral energyprofiles (e.g., FFTs). These spectral energy profiles may be overlappingin range. These set of range based FFTs may be used to analyze thesignals and determine if any additional transducer (XDR) or systemcompensation is required prior to calculating the spectral slope.

The spectral slope is calculated in range. In particular, estimates ofthe frequency dependent attenuation property of the material beingimaged may be calculated. From these measurements an attenuationcoefficient calculation is extracted at various depths. The attenuationcoefficient at increasing depths is used to compensate the spectralproperties of the image with increasing range provided there issufficient signal to noise ratio at the higher frequencies.

FIG. 4 shows a flowchart of a method 400 for obtaining an estimate ofdepth varying spectral characteristics of a transmitted waveform. Themethod 400 shown in FIG. 4 can be used to estimate attenuationcharacteristics across a range of depths of a tissue according to thetechniques described herein.

With reference to the method 400 shown in FIG. 4 , a known transmitpulse spectrum 410 is transmitted and received at a range of depths fromthe tissue. Digitized signals received from the rage of depths from thetissue can then be processed in the image formation stage, at 420. Anumber of image formation techniques can be used to form one or moreimages at the image formation stage 420, such as standard digitalbeamformation, synthetic aperture techniques, and adaptive imageformation techniques. At 430, gain compensation is applied.Specifically, an initial gain compensation is applied at 430 tocompensate for the attenuation properties of the tissue. After gaincompensation is applied at 430, a moving block FFT is applied, at 440,to the signals to generate a set of overlapping spectral energyprofiles, e.g. a set of range-based FFTs. After the set of range-basedFFT's are generated at 440, data compensation, e.g. additionaltransducer (XDR)/system compensation, can be applied at 450.Specifically, at 450, the signals can be analyzed to determine if anyadditional XDR or other applicable system compensation should be appliedprior to calculating one or more spectral slop. At 460, a spectral slopeis calculated within a range of depths. The spectral slope can becalculated, as discussed previously, by estimating frequency dependentattenuation properties of the tissue at various points within a field ofview. At 470, one or more attenuation coefficients can be extracted atvarious depths and/or lateral positions within the tissue based on thespectral slope calculated at 460. Subsequently, the attenuationcoefficient can be reported to the user as either or both a pointestimate and a representation in a color coded regional map ofattenuation within a specified field of view.

FIG. 5 is a graph illustrating a plot 500 of the mean of the varyingdepth spectral estimates of a transducer, in accordance to variousaspects of the subject technology. In particular, the mean estimate ofthe error of a flat spectral response of the transducer is shown by thesolid line 510. It should be noted that the transducer used to makethese measurements was a linear transducer that had a spectral frequencyrange from about 3 MHz to 8 MHz. The spectral estimates of thetransducer response for varying depths are shown by the dotted lines520. From frequencies of about 2 MHz to about 8 MHz the variance of theindividual depth varying spectral responses 520 is fairly constantaround the mean spectral response 510. Above and below these frequenciesthe variance diverges quickly. The reason for the tighter variance atthe lower frequencies is that the overall signal to noise is was higheras the medium that the signals were transmitted into attenuates higherfrequency signals more than lower frequency signals so the signal tonoise ratio (SNR) of the signal decrease as the frequency increases. Asa result, various aspects of the subject technology enable a wider rangeof frequencies of a transducer to be useful and increases the accuracyand image resolution across the transducer.

FIG. 6 is a set of graphs 600 including a set of plots showing thespectral characteristics of a given depth from data uncompensated fortransducer bandwidth limitation and one compensated. Theuncompensated/raw spectral data in range is plotted in graph 610. Thespectrum of this plot 612 of graph 610 shows a reasonably linearattenuation in dB with respect to frequency in the mid band of thetransducer but the overall spectral fit to a line at the lowerfrequencies is minimal. A least mean squares fit line 611 using only thecentral portion of the spectrum for a given range of theuncompensated/raw spectral response is also shown in graph 610. Asillustrated in graph 610, there is substantial roll off of the spectralsignal at lower frequencies even though the overall SNR is good due tolimitations of the transducer's frequency response characteristics.

A plot 622 of the compensated spectral data in range is plotted in graph620. From this plot there is a significantly better linear attenuationin dB to frequency as compared to the uncompensated spectrum shown ingraph 610. Also given the improved lower frequency spectralcharacteristics a least means squares fit line 621 is also improved.Comparing these two graphs 610 and 620 helps to illustrate thatextracting the spectral response of the transducer and compensating fornon-idealities in spectrum based on information contained within thespectral response of the transducer along a set of range samples is ableto improve the overall systems spectral performance of varioustransducers and ultrasound systems.

FIG. 7 shows example attenuation data estimates 700, e.g. attenuationcharacteristics, from a dual attenuation phantom (CIRS 040GSE) with anominal attenuation of 0.5 dB/cm/MHz and 0.7 dB/cm/MHz.

On the left, a plot 710 of the phantom region of a nominal attenuationof 0.5 dB/cm/MHz is shown. The plot 710 includes an estimate of theattenuation of the signal spectral slope 711 of the signal at variousdepths. It can be seen that there is a reasonable consistent slope ofthe spectral slope from the surface to about 60 mm where the overall SNRof the signal becomes dominated by noise. As discussed previously, thedepth at which noise becomes dominant can be increased by using a longerchirp pulse or averaging coherently several transmit receive cycles, asdiscussed previously. A least means square fit across this region, 712,shows that the slope of the least means square fit corresponds to anestimate of attenuation of about 0.5426 dB/cm/MHz or within thetolerance of the phantom manufacturer.

On the right, a plot 720 of the phantom region of a nominal attenuationof 0.7 dB/cm/MHz is shown. The plot 720 includes an estimate of theattenuation of the signal spectral slope 721 of the signal at variousdepths. It can be seen that there is a reasonable consistent slope ofthe spectral slope from the surface to about 40 mm where the overall SNRof the signal becomes dominated by noise. As discussed previously, thedepth at which noise becomes dominant can be increased by using a longerchirp pulse or averaging coherently several transmit receive cycles. Aleast means square fit across this region, 722, shows that the slope ofthe least means square fit corresponds to an estimate of attenuation ofabout 0.7466 dB/cm/MHz or within the tolerance of the phantommanufacturer.

This disclosure has been made with reference to various exemplaryembodiments including the best mode. However, those skilled in the artwill recognize that changes and modifications may be made to theexemplary embodiments without departing from the scope of the presentdisclosure. For example, various operational steps, as well ascomponents for carrying out operational steps, may be implemented inalternate ways depending upon the particular application or inconsideration of any number of cost functions associated with theoperation of the system, e.g., one or more of the steps may be deleted,modified, or combined with other steps.

While the principles of this disclosure have been shown in variousembodiments, many modifications of structure, arrangements, proportions,elements, materials, and components, which are particularly adapted fora specific environment and operating requirements, may be used withoutdeparting from the principles and scope of this disclosure. These andother changes or modifications are intended to be included within thescope of the present disclosure.

The foregoing specification has been described with reference to variousembodiments. However, one of ordinary skill in the art will appreciatethat various modifications and changes can be made without departingfrom the scope of the present disclosure. Accordingly, this disclosureis to be regarded in an illustrative rather than a restrictive sense,and all such modifications are intended to be included within the scopethereof. Likewise, benefits, other advantages, and solutions to problemshave been described above with regard to various embodiments. However,benefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, a required, or anessential feature or element. As used herein, the terms “comprises,”“comprising,” and any other variation thereof, are intended to cover anon-exclusive inclusion, such that a process, a method, an article, oran apparatus that comprises a list of elements does not include onlythose elements but may include other elements not expressly listed orinherent to such process, method, system, article, or apparatus. Also,as used herein, the terms “coupled,” “coupling,” and any other variationthereof are intended to cover a physical connection, an electricalconnection, a magnetic connection, an optical connection, acommunicative connection, a functional connection, and/or any otherconnection.

Those having skill in the art will appreciate that many changes may bemade to the details of the above-described embodiments without departingfrom the underlying principles of the invention. The scope of thepresent invention should, therefore, be determined only by the followingclaims.

What is claimed is:
 1. A method for estimating attenuationcharacteristics of a tissue using ultrasound comprising: receiving echodata corresponding to a detection of echoes of one or more ultrasoundsignals transmitted into the tissue, wherein the echoes are receivedfrom a range of depths of the tissue; obtaining spectral measurementsacross the range of depths of the tissue using the echo data; estimatingattenuation characteristics of the tissue across the range of depths ofthe tissue using the spectral measurements across the range of depths ofthe tissue and known spectral characteristics of the one or moreultrasound signals transmitted into the tissue; averaging the echo datagenerated by the one or more ultrasound signals based on a depth of theone or more ultrasound signals in the tissue where the echo data iscreated; and obtaining the spectral measurements across at least part ofthe range of depths of the tissue using the averaged echo data.
 2. Themethod of claim 1, wherein the known spectral characteristics includemagnitudes of the one or more ultrasound signals across a range offrequencies.
 3. The method of claim 1, wherein the spectral measurementscorrespond to overlapping depth levels across the range of depths of thetissue, the method further comprising averaging the spectralmeasurements at the overlapping depth levels.
 4. The method of claim 1,wherein the echo data is filtered to decrease a central frequency of theecho data as the one or more ultrasound signals used to generate theecho data pass further into the tissue along the range of depths of thetissue.
 5. The method of claim 1, further comprising averaging thespectral measurements across at least a portion of the range of depthsof the tissue to improve an overall signal to noise ratio across therange of depths of the tissue.
 6. The method of claim 1, wherein the oneor more ultrasound signals transmitted into the tissue are formed by oneor more broadband pulses.
 7. The method of claim 1, further comprising:generating a set of fast Fourier transforms based on the echo data,wherein each fast Fourier transform in the set of fast Fouriertransforms is associated with a depth value; generating a set of pointestimates for a frequency dependent filtering coefficient included aspart of the spectral measurements across the range of depths of thetissue, where each point estimate in the set of point estimatescorresponds to a fast Fourier transform in the set of fast Fouriertransforms; and extracting a set of attenuation coefficients based onthe set of point estimates for the frequency dependent filteringcoefficient, the set of attenuation coefficients representing one ormore attenuation levels of the tissue across the range of depths of thetissue.
 8. The method of claim 7, wherein the set of attenuationcoefficients is extracted based on a slope of at least one line formedfrom the set of point estimates.
 9. The method of claim 7, furthercomprising applying a least means square fit on the set of attenuationcoefficients to generate a varying estimate of attenuation levels acrossthe range of depths of the tissue.
 10. The method of claim 7, furthercomprising: generating one or more ultrasound images using the echodata; and applying a moving block fast Fourier transform to the one ormore ultrasound images to generate the set of fast Fourier transforms.11. The method of claim 10, wherein the moving block fast Fouriertransform is applied to the one or more ultrasound images is applied ina direction along the range of depths of the tissue.
 12. The method ofclaim 10, wherein one or more fast Fourier transforms in the set of fastFourier transforms are generated to have overlap with one or moreprevious fast Fourier transforms created by applying the moving blockfast Fourier transform.
 13. The method of claim 10, further comprisingapplying a plurality of moving block fast Fourier transforms to the oneor more ultrasound images to generate the set of fast Fouriertransforms, wherein the set of fast Fourier transforms includes aplurality of subsets of fast Fourier transforms and each subset of fastFourier transforms corresponding to one of the plurality of moving blockfast Fourier transforms used to create each subset of fast Fouriertransforms.
 14. The method of claim 13, further comprising averaging theset of attenuation coefficients based on the plurality of subsets offast Fourier transforms to estimate the one or more attenuation levelsof the tissue across the range of depths of the tissue.
 15. The methodof claim 7, wherein the set of attenuation coefficients is extractedbased on the set of point estimates for the frequency dependentfiltering coefficient included as part of the spectral measurementsacross the range of depths of the tissue and the known spectralcharacteristics of the one or more ultrasound signals transmitted intothe tissue.
 16. The method of claim 1, further comprising: averaging theecho data generated by the one or more ultrasound signals based on adepth of the one or more ultrasound signals in the tissue where the echodata is created.
 17. A system for estimating attenuation characteristicsof a tissue using ultrasound comprising: one or more processors; and acomputer-readable medium providing instructions accessible to the one ormore processors to cause the one or more processors to performoperations comprising: receiving echo data corresponding to a detectionof echoes of one or more ultrasound signals transmitted into the tissue,wherein the echoes are received from a range of depths of the tissue;obtaining spectral measurements across the range of depths of the tissueusing the echo data; estimating attenuation characteristics of thetissue across the range of depths of the tissue using the spectralmeasurements across the range of depths of the tissue and known spectralcharacteristics of the one or more ultrasound signals transmitted intothe tissue; averaging the echo data generated by the one or moreultrasound signals based on a depth of the one or more ultrasoundsignals in the tissue where the echo data is created; and obtaining thespectral measurements across at least part of the range of depths of thetissue using the averaged echo data.
 18. The system of claim 17, whereinthe operations further comprise: identifying areas in the range ofdepths of the tissue represented by speckle in the spectralmeasurements; and continuing to obtain additional spectral measurementsfor the areas.
 19. The system of claim 17, wherein the operationsfurther comprise averaging the spectral measurements across at least aportion of the range of depths of the tissue to improve an overallsignal to noise ratio across the range of depths of the tissue.
 20. Thesystem of claim 17, wherein the spectral measurements correspond tooverlapping depth levels across the range of depths of the tissue, theoperations further comprising averaging the spectral measurements at theoverlapping depth levels.